The present invention relates to optimization systems for determining how best to cut a piece of raw material, such as lumber, so as to produce the optimal yield.
For some time now, in the lumber industry, computer-driven scanning systems have been used to increase the yield of finished lumber and veneer from logs at various stages of processing. Such systems are disclosed, for example, in the following U.S. Pat. Nos. 3,736,968; 3,746,065; 3,787,700; 3,852,579; 3,890,509; 3,902,539; 3,992,615; 4,197,888; 4,221,973; 4,397,343; and 4,803,371.
In these systems, the computer is used to compute how best to cut each piece of raw material so as to obtain the highest yield. These computations are based on the measured dimensions of the piece and a model generated internally by the computer that characterizes the entire geometry of the piece. Once the computer has determined the best cutting option, this option is implemented either by computer-controlled or by manually operated cutting equipment.
In a typical set-up, for example, the log, already precut to the standard 8-foot length, rides on a headrig carriage, which resembles a small flatbed railroad car with special log clamps on top, so as to pass across an optical or other noncontact scanning array. The horizontal dimension of the log is measured by the scanner along an axis parallel to the carriage track and this measurement is processed by the computer which derives the vertical and remaining dimensions of the log based on a circular model. From the full set of parameters derived, the computer then determines which cuts to make.
For example, referring to FIG. 1, the computer may determine that the log 18 shown should be cut along paths 12-16, thereby producing standard-shaped slabs known as a cant 20 and a flitch 22. These slabs are then cut again to produce the standard-sized boards 24 indicated in FIG. 2. Alternatively, the computer may determine that when making the cut the centerline of the cant should be offset from the centerline of the log by a predetermined distance 26 (FIG. 1) such as 0.3 inches. This may yield, after further processing, the collection of standard-sized boards 28 indicated in FIG. 3. Comparing FIGS. 2 and 3, it will be recognized that the computer-selected offset implemented in FIG. 3 has yielded increased value because a more valuable 2.times.6 board 28a (FIG. 3) has been substituted for a less valuable 2.times.4 board 24a (FIG. 2). This difference in yield can conveniently be expressed as a difference in fair market price for the finished-cut lumber. Accordingly, by reading the measured dimensions of a particular log, in advance of processing, the computer can be used to select the cutting options that offer the highest projected yield and to project the yield available from particular purchased lots of timber.
The difficulty with existing systems, however, is that frequently the computer will project a yield for a given cutting option that does not match the overall yield that is actually realized when the cutting option is implemented. This mismatch between theory and practice occurs because of system uncertainty associated with either measurement, modeling, or implementation.
For example, measured values may be faulty due to poor calibration of the scanning equipment, poor mechanical presentation of the wood to the sensor, or poor mounting of the wood on the conveying system. In particular, instead of a headrig carriage, often a sharp chain conveyor is used which holds the log on spikes that project from a moving chain. While this setup permits the log to be measured along two axes, often the log will rock on the chain as it travels through the scan zone. Similarly, a flitch riding on a flatchain conveyor may slip during the measurement process.
Modeling error arises because the set of dimensional parameters derived by the computer, in accordance with its model and whatever number of measurements are practical, may not accurately reflect the true dimensions of the log. In the above-described set-up, for example, based on its circular model, the computer assumes that the vertical dimension of the log is equal to its measured horizontal dimension but, in fact, the log may be elliptical in shape. Referring to FIG. 4, even if the computer assumes an elliptical model 30 and bases its derived values on measurements taken along two axes 32 and 34, there are still likely to be deviations in the true shape 36 of the log from the model. Other types of modeling error can arise when the bark is left on the log so that the thickness of the underlying wood can only be estimated. A similar type of problem occurs if the log is known to consist of a layer of premium-grade material surrounding a core of standard-grade material and the thickness of the premium-grade material is uncertain.
Implementation error results because the cutting option specified by the computer may not be carried out exactly in the manner envisioned. For example, after a flitch has been scanned and its optimal cut identified, the sloped edge 38 or "wane" of the flitch (FIG. 1) may be crushed a bit as it is positioned on the in-feed table of the automated edger. The edger then cuts the flitch at the computer-specified distance, but with reference to this crushed edge instead of to the original edge, thereby producing an offset from the cutting path envisioned. Alternatively, a manual edger may follow the headrig, and the edgerman may choose to edge the flitch in a way different than that envisioned by the headrig computer.
In existing optimization systems, to deal with the inherent uncertainties involved, it has been the practice, in projecting yield, to rely on the most likely set of events. Usually this is done implicitly, without any added computational effort, as part of the basic assumptions used in writing the program for the computer. In the above-described setup, for example, the computer relies on the assumption that the most likely cross-sectional profile of the log is a circle. Although some of the logs will, in fact, have a vertical dimension falling far short of their horizontal dimension, and some of the logs, will, in fact, have a vertical dimension far exceeding that of their horizontal, it is generally believed that, on balance, such deviations will tend to average out and that many of the logs will most likely have a vertical dimension about equal to their horizontal. Relying on such assumptions, existing systems compute the most likely yield for a given cutting option and adopt such yield as the relative value of that cutting option. This approach to determining the value of a given cutting option is summarized in blocks 38 and 40 of FIG. 5.
Despite the apparent reasonableness of this approach, however, it has often been found, in actual practice, that when the optimized cutting solutions are actually implemented, the overall yield actually realized deviates significantly from that projected by the optimization system. Moreover, it has also been found that manual operators, in deviating from the "optimal" cutting solution selected by the computer, will sometimes produce overall yields that are higher than those obtained when the cutting solution was followed.
Accordingly, an object of the present invention is to provide an improved method of optimizing the overall yield of useful material cut from a piece of raw material.
A related object of the present invention is to provide a method for projecting the yield of a batch of logs where such yield will closely match the overall yield that is later obtained once the logs are actually cut.
Another related object of the present invention is to provide a method for identifying which of a number of cutting options will actually produce the highest overall yield in practice.
Yet another related object of the present invention is to provide a method of compensating for the specific underlying conditions affecting the yield in a given system.
Still another object of the present invention is to achieve a more effective blending of automated systems and manual systems in the same factory.